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Artificial neural network models for predicting patterns in auditing monthly balances

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  • E Koskivaara

    (Turku Centre for Computer Science and Turku School of Economics and Business Administration)

Abstract

The aim of this study is to investigate the potential of artificial neural network (ANN) models to recognise patterns when auditing monthly balances in financial accounts. ANNs have been used in many different disciplines as a basis for building intelligent information systems. This study examines the predictive ability of an ANN by building models using the 72 monthly balances of a manufacturing firm. The monthly balances are regarded as a time-series and the target is to recognise the dynamics and the relationships between different accounts. Furthermore, a certain seeded material error with signals from the ANN model is investigated. The results achieved indicate that neural networks seem promising for recognising the dynamics and the relationships between financial accounts.

Suggested Citation

  • E Koskivaara, 2000. "Artificial neural network models for predicting patterns in auditing monthly balances," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(9), pages 1060-1069, September.
    • Handle: RePEc:pal:jorsoc:v:51:y:2000:i:9:d:10.1057_palgrave.jors.2601014
    DOI: 10.1057/palgrave.jors.2601014
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    Citations

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    Cited by:

    1. Lin, Chin-Shien & Khan, Haider A. & Chang, Ruei-Yuan & Wang, Ying-Chieh, 2008. "A new approach to modeling early warning systems for currency crises: Can a machine-learning fuzzy expert system predict the currency crises effectively?," Journal of International Money and Finance, Elsevier, vol. 27(7), pages 1098-1121, November.
    2. R Setiono & S-L Pan & M-H Hsieh & A Azcarraga, 2006. "Knowledge acquisition and revision using neural networks: an application to a cross-national study of brand image perception," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(3), pages 231-240, March.
    3. Amelia A. Baldwin & Carol E. Brown & Brad S. Trinkle, 2006. "Opportunities for artificial intelligence development in the accounting domain: the case for auditing," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 14(3), pages 77-86, July.
    4. Amani, Farzaneh A. & Fadlalla, Adam M., 2017. "Data mining applications in accounting: A review of the literature and organizing framework," International Journal of Accounting Information Systems, Elsevier, vol. 24(C), pages 32-58.

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